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A Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain

https://doi.org/10.1007/11790853_9
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Multi-relational Data Mining, Using UML for ILP
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Case-Based Reasoning Technology
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Advances in Case-Based Reasoning
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SpamHunting: An instance-based reasoning system for spam labelling and filtering
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BoosTexter: A Boosting-based System for Text Categorization
inferredcites →https://doi.org/10.1023/a:1008287121180openalex-snapshot
The State of the Art in Text Filtering
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A Memory-Based Approach to Anti-Spam Filtering for Mailing Lists
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Support vector machines for spam categorization
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Inductive learning algorithms and representations for text categorization
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Handling concept drifts in incremental learning with support vector machines
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Machine learning in automated text categorization
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Word association norms, mutual information, and lexicography
inferredcites →https://doi.org/10.48550/arxiv.cs/0009009openalex-snapshot
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
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Advances in kernel methods: support vector learning
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Word association norms, mutual information, and lexicography
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Fast Training of Support Vector Machines Using Sequential Minimal Optimization

What points at this work

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